Model Explainer

Feature Importances

Feature Importances

Model performance metrics

metric Score
accuracy 0.808
precision 0.754
recall 0.975
f1 0.851
roc_auc_score 0.837
pr_auc_score 0.812
log_loss 0.429

Confusion Matrix

How many false positives and false negatives?

Precision Plot

Does fraction positive increase with predicted probability?

Classification Plot

Distribution of labels above and below cutoff

ROC AUC Plot

Trade-off between False positives and false negatives

PR AUC Plot

Trade-off between Precision and Recall

Lift Curve

Performance how much better than random?

Cumulative Precision

Expected distribution for highest scores

Individual Predictions

Select Random Index

Selected index: 121123

Prediction

label probability logodds
0* 15.4 % -1.705
1 84.6 % 1.705

Contributions Plot

How has each feature contributed to the prediction?

Partial Dependence Plot

Contributions Table

How has each feature contributed to the prediction?
Reason Effect
Average of population -0.19
Previously_Insured = 0.0 +0.76
Vehicle_Damage_Yes = 1.0 +0.54
Vehicle_Age_lt_1_Year = 0.0 +0.24
Vehicle_Age_gt_2_Years = 1.0 +0.23
Policy_Sales_Channel = 26.0 +0.16
Age = 0.34260429820518445 -0.09
Gender = 1.0 +0.06
Vintage = 0.6299655402446693 -0.0
Annual_Premium = 0.08856725608565025 -0.0
Region_Code = 28.0 -0.0
Other features combined +0.0
Final prediction 1.71

What if...

Select Random Index

Selected index: 225339

Prediction

label probability logodds
0 19.9 % -1.391
1 80.1 % 1.391

Feature Input

Adjust the feature values to change the prediction

Contributions Plot

How has each feature contributed to the prediction?

Partial Dependence Plot

Contributions Table

How has each feature contributed to the prediction?
Reason Effect
Average of population -0.19
Previously_Insured = 0.0 +0.76
Vehicle_Damage_Yes = 1.0 +0.54
Vehicle_Age_lt_1_Year = 0.0 +0.24
Age = -0.43509549375910955 +0.17
Policy_Sales_Channel = 124.0 -0.06
Gender = 0.0 -0.06
Vehicle_Age_gt_2_Years = 0.0 -0.02
Vintage = -1.6952691812469631 +0.01
Region_Code = 28.0 -0.0
Annual_Premium = 0.04603421172574809 +0.0
Other features combined +0.0
Final prediction 1.39

Feature Dependence

Shap Summary

Ordering features by shap value

Shap Dependence

Relationship between feature value and SHAP value